Search Results (1,106)

The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets. Full article

Different microstructures significantly affect the acoustic performance of MEMS capacitive microphones, particularly in key specifications of interest. This paper presents several microstructures, including rib-reinforced backplates, suspended diaphragms, and outer vent holes. Three MEMS microphone designs were implemented to analyze the impact of these microstructures. Equivalent circuit models corresponding to each design were constructed to simulate specifications such as sensitivity, signal-to-noise ratio (SNR), and low corner frequency (LCF), which were validated through experimental measurements. Finite Element Analysis (FEA) was also employed to calculate the acoustic damping of certain microstructures, the mechanical lumped parameters of the diaphragm, and the pre-deformation of the MEMS structure. A compressed air test (CAT) was conducted to evaluate the mechanical reliability of microphone samples. The results of simulations and measurements indicate that rib-reinforced backplates effectively improve microphone reliability, increasing the pass rate in CAT. Compared to fully clamped diaphragms, suspended diaphragms exhibit higher mechanical compliance, which enhances SNR performance but reduces AOP. Outer vent holes can achieve similar functionality to diaphragm vent holes, but their impact on improving AOP requires further design and testing. Full article

This paper proposes a novel blind channel estimation method using convolutional neural network (CNN)-based resource grouping. The traditional K-means-based blind channel estimation scheme suffers limitations in reflecting fine-grained channel variations in both the time and frequency domains. To address these limitations, we propose dynamic resource grouping based on CNN architecture utilizing a two-step learning process that adapts to various channel conditions. The first step of the proposed method identifies the optimal number of subcarriers for each channel condition, providing a foundation for the second step. The second step adjusts the number of orthogonal frequency division multiplexing (OFDM) symbols, a parameter for determining the proposed pattern in the time domain, to adapt to dynamic channel variations. Simulation results demonstrate that the proposed CNN-based blind channel estimation method achieves high channel estimation accuracy across various signal-to-noise ratio (SNR) levels, attaining the highest accuracy of 82.5% at an SNR of 10 dB. Even when classification accuracy is relatively low, the CNN effectively mitigates signal distortion, delivering superior performance compared to conventional methods in terms of mean squared error (MSE) across diverse channel conditions. Notably, the proposed method maintains robust performance under high-mobility scenarios and severe channel variations. Full article

In the field of direction of arrival (DOA) estimation, a common assumption is that array noise follows a uniform Gaussian white noise model. However, practical systems often encounter non-ideal noise conditions, such as non-uniform or colored noise, due to sensor characteristics and external environmental factors. Traditional DOA estimation techniques experience significant performance degradation in the presence of colored noise, necessitating the exploration of specialized DOA estimation methods for such environments. This study introduces a DOA estimation method for acoustic vector arrays based on a spectrum attention mechanism (SAM). By employing SAM as an adaptive filter and constructing a double-branch model combining a convolutional neural network (CNN) and long short-term memory (LSTM), the method extracts the spatial and temporal features of signals, and effectively reduces the frequency components of colored noise, enhancing DOA estimation accuracy in colored noise scenarios. At an SNR of −5 dB, it achieves an accuracy rate of 85% while maintaining a low RMSE of only 2.03°. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article

Since its launch in 2018, the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has provided atmospheric products, including calibrated backscatter profiles and cloud and aerosol layer detection. While not the primary focus of the mission, these products garnered more interest after the end of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data collection in 2023. In comparing the cloud and aerosol detection frequencies from CALIOP and ICESat-2, we find general agreement in the global patterns. The global cloud detection frequencies were similar in June, July, and August of 2019 (64.7% for ICESat-2 and 59.8% for CALIOP), as were the location and altitude of the tropical maximum; however, low daytime signal-to-noise ratios (SNRs) reduced ICESat-2’s detection frequencies compared to those of CALIOP. The ICESat-2 global aerosol detection frequencies were likewise lower. ICESat-2 generally retrieved a higher average global aerosol optical depth compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) over the ocean, but the two were in closer agreement over regions with higher aerosol concentrations such as the Eastern Atlantic Ocean and the Northern Indian Ocean. The ICESat-2 and CALIOP orbital coincidences reveal highly correlated backscatter profiles as well as similar cloud and aerosol layer top altitudes. Future work with machine learning denoising techniques may allow for improved feature detection, especially during daytime. Full article

The generalized Radon–Fourier transform (GRFT) is a well-established coherent accumulation technique for high-speed and high-mobility target detection. However, this method tends to suffer from the difficulty of identifying the main lobe from multiple blind speed sidelobes (BSSLs) and the computational complexity is generally high. To address these challenges, we propose a new method, namely the BSSL Traction Particle Swarm Optimization (BTPSO), to robustly and accurately extract the main lobe. In the method, the relationship between the main lobe and the BSSLs is used to attract particles to potential positions of the main lobe in the group when trapped in local optimal, and a new termination criterion in which multiple particles should converge to the same optimal value is proposed to avoid local convergence. Simulation examples show that the proposed method can improve the probability of converging to the main lobe peak while reducing cost time, and its good adaptability to low signal-to-noise ratio (SNR) cases is well verified. Full article

Software-defined radio (SDR) has high flexibility and low cost. It conforms to the miniaturization, lightweight, and digitization trends in through-wall radar systems. Stepped frequency continuous waveform (SFCW) is commonly used in through-wall radar, which has high resolution and strong anti-interference ability. This article develops an SFCW through-wall radar system based on a dual-channel SDR platform. Without changing hardware structure and complicated accessories, a phase compensation method of solving the phase incoherence problem in a low-cost dual-channel SDR platform is proposed. In addition, this article proposes a wall clutter mitigation approach by means of singular value decomposition (SVD) and principal component analysis (PCA) framework for through-wall applications. This approach can process the wall clutter and noise ‌efficiently,‌ and then extract the target subspace to obtain location information. The experimental results indicate that the proposed windowing-based SVD-PCA approach is effective for the developed radar system, which can ensure the accuracy of through-wall detection. It is also superior to the traditional methods in terms of the image quality of range profiles or signal-to-noise ratio (SNR). Full article

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed. Full article

Due to their compact sizes, low power consumption levels, and convenient integration capabilities, piezoelectric micromachined ultrasonic transducers (PMUTs) have gained significant attention for enabling environmental sensing functionalities. However, the frequency inconsistency of the PMUT arrays often leads to directional errors with the ultrasonic beams. Herein, we propose a reconfigurable PMUT array based on a Sc_0.2Al_0.8N piezoelectric thin film for in-air ranging. Each element of the reconfigurable PMUT array possesses the ability to be independently replaced, enabling matching of the required frequency characteristics, which enhances the reusability of the device. The experimental results show that the frequency uniformity of the 2 × 2 PMUT array reaches 0.38% and the half-power beam width (θ_−3dB) of the array measured at 20 cm is 60°. At a resonance of 69.7 kHz, the sound pressure output reaches 7.4 Pa (sound pressure level of 108.2 dB) at 19 mm, with a reception sensitivity of approximately 11.6 mV/Pa. Ultimately, the maximum sensing distance of the array is 7.9 m, and it extends to 14.1 m with a horn, with a signal-to-noise ratio (SNR) of 19.5 dB. This research significantly expands the ranging capability of PMUTs and showcases their great potential in environmental perception applications. Full article

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network. Full article

Solar-induced chlorophyll fluorescence (SIF) is essential for monitoring vegetation photosynthesis. The water vapor absorption band, the broadest absorption window, has a deeper absorption line than the O₂-B band, providing significant potential for SIF retrieval; however, substantial variation in atmospheric water vapor column concentrations limits research on SIF retrieval using this band. This study evaluates seven common SIF retrieval algorithms, including sFLD, 3FLD, iFLD, pFLD, SFM, SVD, and DOAS, using simulated datasets under varying atmospheric water vapor concentrations, spectral resolution (SR), and signal-to-noise ratios (SNRs). Additionally, the SIF retrieval results from the H₂O, O₂-B, and O₂-A absorption bands are compared and analyzed to explore the fluorescence retrieval potential of the water vapor band. Furthermore, the potential of commonly used spectrometers, including Ocean Optics QE Pro and ASD FieldSpec 3, for SIF retrieval using the water vapor absorption band was evaluated. The results were further validated using ground-based tower observations. The results show that sFLD consistently overestimates SIF in the water vapor band, limiting its reliability, while SFM performs best across varying conditions. In comparison, 3FLD and pFLD, along with SVD, are accurate at high resolutions but less effective at lower ones. iFLD performs relatively poorly overall, whereas DOAS excels in low SR retrievals. At the same time, our study also shows that the water vapor band offers higher accuracy in ground-based SIF retrieval compared to the O₂-B band, demonstrating strong application potential and providing valuable references for selecting SIF retrieval algorithms. Full article

This study presents the optimization of two SPR biosensors, Sys₃ and Sys₅, for SARS-CoV-2 detection at concentrations of 0.01–100 nM. Sys₃, with a 55 nm silver layer, a 13 nm silicon nitride layer, and a 10 nm ssDNA layer, achieved a figure of merit (FoM) of 571.24 RIU⁻¹, a signal-to-noise ratio (SNR) of 0.12, and a detection accuracy (DA) of 48.93 × 10⁻². Sys₅, incorporating a 50 nm silver layer, a 10 nm silicon nitride layer, a 10 nm ssDNA layer, and a 1.6 nm tungsten disulfide layer (L = 2), demonstrated a higher sensitivity of 305.33 °/RIU and a lower limit of detection (LoD) of 1.65 × 10⁻⁵. Sys₃ outshined in precision with low attenuation (<1%), while Sys₅ provided enhanced sensitivity and lower detection limits, crucial for early-stage viral detection. These configurations align with the refractive index ranges of clinical SARS-CoV-2 samples, showcasing their diagnostic potential. Future work will focus on experimental validation and integration into point-of-care platforms. Full article

Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) holds significant importance in vehicle-to-everything (V2X) communications, with its main advantages being outstanding spectral efficiency and strong interference resistance. However, the existing OFDM-IM systems in vehicular networks overlook actual vehicular network channels and the impact of scatterers, thus failing to accurately reflect the system performance. Moreover, these systems focus solely on the bit error rate (BER) and ignore user requirements for low energy consumption and high spectral efficiency. To address these issues, we propose a user demand- and scenario-adaptive OFDM-IM method that optimizes the OFDM-IM index parameter by considering the spectral efficiency, BER, and energy consumption. Firstly, considering non-line-of-sight components and roadside reflectors, we establish a vehicle-to-vehicle (V2V) communication channel model for straight road scenarios. Then, we construct a transmission framework for vehicular network communication using OFDM-IM. Specifically, we develop an energy efficiency maximization formula, in which fuzzy logic is used to adjust the weights of the three performance indicators to meet various environmental and user requirements. In detail, we discuss the minimum signal-to-noise ratio (SNR) required for OFDM-IM to achieve a lower BER than traditional OFDM in various vehicular communication scenarios. Thus, we can make appropriate choices based on the robustness of the simulation results. The simulation results presented in this paper indicate our method’s effectiveness in enhancing the system’s reliability, efficiency, and flexibility. Full article

P300 detection is a difficult task in brain–computer interface (BCI) systems due to the low signal-to-noise ratio (SNR). In BCI systems, P300 waves are generated in electroencephalogram (EEG) signals using various oddball paradigms. Convolutional neural networks (CNNs) have previously shown excellent results for P300 detection compared to different machine learning models. However, current CNN architectures limit P300 detection accuracy because these models usually only extract single-scale features. Aiming to enhance P300 detection accuracy, an inception module-based CNN architecture, namely Inception-CNN, is introduced. Inception-CNN effectively learns discriminative features from both spatial and temporal information to reduce overfitting and computational complexity. Furthermore, it can extract multi-scale features, which effectively improves P300 detection accuracy and increases character spelling accuracy. To analyze the effect of the inception layer, two additional models are proposed: Inception-CNN-S, which uses the inception layer with a spatial convolution layer, and Inception-CNN-T, which uses the inception layer with a temporal convolution layer. The proposed model was evaluated on dataset II of BCI Competition III and dataset IIb of BCI Competition II. The experimental results show that Inception-CNN provides a promising solution for improving the accuracy of P300 detection, with F1 scores of 47.14%, 55.28%, and 78.94% for dataset II of BCI Competition III (Subject A and Subject B) and dataset IIb of BCI Competition II, respectively. Full article